Network services like so called recommender systems are today relative well known and are used in different services for recommending various products including media such as movies, music, pictures and books, for example via on-line sales companies like Amazon.com or Internet radio and music community websites like Last.fm.
A commonly used recommender method used in such systems is collaborative filtering which can generate recommendations by computing a similarity between users' preferences for a specific product. Another well known recommender method is the content-based recommender method. In brief, the recommendations are often based on the product description or meta data such as title, description, any static data etc. for the product which may be mapped to a profile or preferences of a certain user visiting a service that utilizes a recommender system.
More specific recommender systems focus on collaborative filtering dealing with self-organizing communities, host mobility, wireless access, and ad-hoc communications. In such domains, knowledge representation and user profiling can be hard as remote servers can often be unreachable due to client mobility. Also, feedback ratings collected during random connections to other users' ad-hoc devices can be useless because of natural differences between persons. To handle these problems a network can be used for epidemically spreading recommendations through spontaneous similarities between users of the network.
Another method for automatic generation of content (product) recommendations is described in US 2008/0134053, where the method provides recommendations for content to a user of a social network service. The method includes: collecting recommendations for content; determining preferences of the user and preferences of the user's social networks; selecting recommendations based on the preferences of the user and the user's social networks; and providing the selected recommendations to the user. For example, content recommendations from a family member or known friend of the user may be highly weighted over other recommendations.
Though the technologies described above may assist in providing a user with a relevant recommendation, they are not always accurate and lack the preciseness of e.g. real life word-of-mouth passing of information.